By Sayantan Ghosh, Head of Credit Risk Management & Data Science, Balancehero India
With a population of 1.4 billion, India’s diversity poses unique challenges for financial services, particularly in extending equitable access across socioeconomic layers. The digital revolution in India—driven by smartphone penetration, internet connectivity, and more inclusive digital payment platforms—has significantly expanded financial access. Fintech firms and NBFCs have seized this opportunity, using artificial intelligence (AI) and machine learning (ML) to reach communities previously excluded from traditional banking services. A notable statistic is that fintech companies now hold a 52% market share in personal loans, primarily catering to non-traditional credit (NTC) individuals and underserved communities with small-ticket loans under ₹50,000. Yet, as demand for credit grows, especially among underserved populations, the real challenge for lenders is balancing inclusivity with responsible credit risk management.
In a rapidly growing economy, AI-powered alternative credit scoring (ACS) systems are proving essential for lenders in making quick, informed lending decisions, particularly for individuals lacking formal credit histories. By leveraging alternative data sources such as mobile app usage, geolocation, and device configurations, ACS models create comprehensive profiles of applicants’ financial capacities and behaviours. This data-driven approach not only accelerates the lending process but also expands credit access to individuals outside conventional models, fostering broader financial inclusion. By March 2024, fintech firms facilitated ₹2,48,006 crore in personal loans, which has been instrumental in driving financial inclusion across rural and semi-urban areas.
ACS systems transform vast amounts of unstructured data into actionable insights, using deep learning algorithms to analyse device or bank statement data and reveal patterns of financial health. When combined with traditional credit bureau data, these insights enable precise assessments of repayment ability, even for applicants with limited credit histories. Machine learning techniques further enhance ACS models by estimating essential financial metrics, such as income or existing debt obligations, without intrusive data collection—ensuring a frictionless experience for applicants. A major strength of AI-driven credit scoring is its adaptability and speed of evolution; ACS models evolve continually, incorporating new data and algorithmic improvements to refine predictive accuracy.
Moreover, AI plays a critical role in fraud detection within the fintech sector. The market for AI technology in fraud detection is anticipated to grow from $2.1 billion in 2019 to $6.5 billion by 2024, reflecting increasing reliance on AI systems to secure financial transactions. By providing a holistic view of customer profiles, these systems empower lenders to manage default risks effectively while maintaining a competitive edge in the dynamic fintech landscape.
The true validation of AI’s impact on financial inclusion lies in improved credit performance. For firms utilising AI-driven scoring, lower default rates reflect the success of responsible lending practices. Year-over-year improvements in credit outcomes highlight how AI and ML can be continuously refined to optimise risk management. By advancing ACS systems, fintech firms not only meet the demand for credit but also contribute to a more inclusive economy. In a diverse and rapidly developing nation like India, AI is transforming how financial institutions empower underserved communities, fostering a fairer financial future. This integration maintains the original flow while incorporating relevant statistics that underscore the transformative impact of AI on fintech lending in India.